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Schizophrenia diagnosis using the GRU-layer's alpha-EEG rhythm's dependability.

Pankaj Kumar Sahu1, Karan Jain1

  • 1Department of Instrumentation and Control Engineering, Dr B R Ambedkar National Institute of Technology, Jalandhar, Punjab, 144008, India.

Psychiatry Research. Neuroimaging
|September 1, 2024
PubMed
Summary

This study introduces the Rudiment Densely-Coupled Convolutional Gated Recurrent Unit (RDCGRU) deep learning model for schizophrenia (SZ) diagnosis using electroencephalography (EEG) rhythms. The RDCGRU model achieved 88.88% accuracy with alpha-EEG rhythms, demonstrating its effectiveness in SZ verification.

Keywords:
Gru: the “gated recurrent unit (gru);” rdcgru: the “rudiment densely-coupled convolutional gru;” dcgru: the “densely-coupled convolutional gru;” Con-1-D layer: “1-d-Convolution layer;” α-EEG rhythm

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Schizophrenia (SZ) diagnosis can be challenging.
  • Deep learning models show promise in analyzing brain activity patterns.
  • Electroencephalography (EEG) rhythms offer potential biomarkers for neurological conditions.

Purpose of the Study:

  • To evaluate the reliability of alpha-EEG rhythms for SZ verification using a deep learning model.
  • To introduce and assess the performance of the Rudiment Densely-Coupled Convolutional Gated Recurrent Unit (RDCGRU) model for SZ diagnosis.
  • To explore the effectiveness of RDCGRU across various EEG rhythms (gamma, beta, alpha, theta, delta).

Main Methods:

  • Development of the RDCGRU deep learning model, incorporating 1-D Convolutional (Con-1-D) layers and Gated Recurrent Unit (GRU) layers.
  • Utilization of Exponential-Linear-Unit activation function for improved training and classification.
  • Application of Densely-Coupled Convolutional Gated Recurrent Unit (DCGRU) layers to mitigate gradient issues.
  • Binary classification using sigmoid activation function in output nodes.

Main Results:

  • The RDCGRU model achieved a highest accuracy of 88.88% when utilizing alpha-EEG rhythms.
  • GRU cells within the RDCGRU model demonstrated superior responsiveness to alpha-EEG rhythms for SZ verification.
  • The model's architecture effectively processed EEG signals, learning to reduce noise and extract relevant patterns.

Conclusions:

  • Alpha-EEG rhythm is a reliable indicator for schizophrenia verification when analyzed with deep learning.
  • The RDCGRU model presents a robust and effective deep learning approach for EEG-based SZ diagnosis.
  • Further development of deep RDCGRU models may enhance diagnostic capabilities for complex neurological conditions.